Thursday, April 7, 2011

We’ve just released XAP 8.0.1, with a lot of goodies included. 8.0.1 is the first feature and service pack on top of XAP 8.0.0. It includes many enhancements and a few exciting new features. Here’s a short recap:

Improved Web UI Dashboard with Alerts View: The dashboard view now gives you a single click view of the entire cluster, including alerts on various problematic conditions. The previous view is now available under the topology tab. This is the first stage in the new Web based UI planned for XAP. You can find more details about it here.

Elastic Deployment for Stateless and Web Processing Units: The elastic deployment model introduced in 8.0 for stateful and data grid only processing units has now been extended to support stateless and web processing units. You can scale web applications and stateless processing units up and down based on CPU, memory or available resources.

Document (Schema-Free) API support for .Net: The .Net edition now includes the all new document API which was introduced in the 8.0.0 in the Java version. It enables you to maintain a completely flexible domain model without any restrictions on the entry's schema, and add/remove properties as your application evolves. It also simplifies interoperability with Java since when using the Document API it's no longer tied to a concrete .Net and Java classes.

Improved complex object querying and indexing for .Net: The .Net edition now enables you to query and index complex object structures, including nested collections and arrays.

Deep POJO/PONO - Document Interoperability: Documents and POJOs can now be mixed interchangeably across all nesting level. You can read a document as a POJO/PONO (assuming its type name corresponds to the POJO/PONO class name) and vice versa. The space will convert between the formats across all nesting levels, so if you have a complex Java object for example which contains a reference to a nested Java Object or a collection of nested objects, the space will convert the entire object graph to documents and sub documents. In addition, you can also define a "bag" of dynamic properties for a certain POJO/PONO so that new properties that are added via the document API to the entry are exposed in the POJO/PONO instance via this bag.

Map/Reduce and Native Query Support for JPA: The XAP JPA Implementation now supports the JPA NativeQuery facility. On top of running queries in the Space's native syntax, it also enables you to actually execute Space tasks across one or all cluster nodes and bring the power of the grid to the JPA API. Tasks can be defined using the GigaSpaces task execution interfaces or even as a dynamic language script for scripting languages that are supported as part of the JVM.

Method Level routing and result reducers for Space Based Remoting: Space Based Remoting has traditionally been a very popular facility to reliably expose scalable business services to your application clients. In 8.0.1, you can specify method level behaviors for the foundational remoting constructs such as RemoteRoutingHandlers and RemoteResultReducers via the dedicated @ExecutorRemotingMethod and @EventDrivenRemotingMethod annotations.

WAN Replication Improvements: 8.0.1 adds a number of important improvements and bug fixes to the replication over WAN module, such as better peer classloading behavior (when the classes written to the space are not part of the space's classpath), better cleanup of replicated entries, and support for replication of .Net entries.

Improved Performance of .Net Executor API: The .Net task execution API has gone some optimization in the way that tasks are passed to the space and executed in it, which resulted in performance boosts of up to 250%.

More JPA goodies: In addition to NativeQuery support, we have also implemented a number of other changes, including better JPQL syntax support (LIKE, IS NULL), optimistic locking support and improved relationship handling.

Improved XA Transaction Support: XA transactions can now work against a partitioned space cluster as a single XA resource (via the distributed Jini transaction manager) rather than working with each partition separately.

Mule 3.1 Support: The build in Mule ESB support has been ungraded to support Mule version 3.1.

Tuesday, March 8, 2011

There has been a great deal of talk lately about the new EHCache cache querying capabilities and the advantages of real-time analytics through in-memory cache querying. I find that rather odd since extensive querying and processing capabilities have been around for years with in memory data grids like GigaSpaces XAP, Oracle Coherence, Gemstone GemFire and more recently Hazelcast and GridGain. So I don’t really understand the big fuss around EHCache finally supporting it….

But that’s actually a great opportunity to revisit some of the work we’ve done in our recent 8.0 release in the context of querying. There are two main features we’ve introduced in 8.0 that take data grid querying to the next level.

Complex Object Querying

The first one is complex object graph querying. Simply put, you can now index and any type of property, at any nesting level, of your data grid objects: primitive types, user defined types, or collections/arrays of either.Think of an Author object, which has a collection of books, each of them containing a collection of user reviews.Here are just two examples of what you can achieve with XAP 8.0:• List all the Authors that wrote at list one sci-fi book:

This goes hand in hand with our new Document support, so you can apply the above to schema free documents as well as just plain Java or .Net objects.

GigaSpaces JPA

The second feature is our all new JPA support. We believe this to be a major step towards ease of use and standardization of distributed data store access.

There have been a lot of discussions in the developer community about how the “classic” querying and interaction models (such as SQL/JDBC and JPA) can be mapped to the world of distributed data stores (which in memory data grids are a part of).

But when developing to each of those products, you need to learn their interaction model, data access semantics (such as transaction management) and of course querying language.

The first take at bridging the two worlds, and IMO pretty successful one considering the inherent difficulties in bridging the two models (see this presentation I’m giving at QCon London in two days), was actually done by Google almighty with the JPA support in AppEngine.Although not 100% standard (actually not even close), it gives you as a developer a very easy and familiar way to implement scalable data access on top of their BigTable data store, and a very clear migration path for putting your apps on AppEngine, should you choose to do so. It also makes your code a lot more portable since JPA, even a partial version of it, is still way more standard and portable than any proprietary API.

GigaSpaces JPA – Design Principles

When we initially thought of implementing a JPA interface to our data grid, we had the following goals in mind:

Make our user’s life easier by providing a standard way for the to interact with the data grid that will allow them to easily get up to speed with new applications, and port existing JEE applications more smoothly and quickly.For that, we created a thin layer on top of the excellent OpenJPA framework so that configuring a GigaSpaces EntityManagerFactory is a breeze if you’re already into JPA in general and OpenJPA in particular. We also managed to support a nice set of features from the JPA specification (actually more extensive than Google’s AppEngine JPA) so that most of the stuff users actually do with JPA is covered. Naturally, this is just our first take at this huge project, and we’ll add more and more capabilities as we move forward.

Enable our users to leverage the power and scalability of the data grid to scale their data access, even when they’re using JPA (which was originally designed for a centralized data model). This means exposing content based routing and data grid indexing to their JPA domain model, and abstracting away cluster wide transactions and queries.

Protect our users from making wrong implementation decisions when modeling and accessing their distributed data. In Google AppEngine’s terminology, this means supporting Owned Relationship only such that object graphs are not scattered across many nodes and are always limited to a single node.

Allow our users to use our powerful native APIs for functions that are not covered by the JPA specification, e.g. event handling, distributed task execution, and more. So it actually means that you can use the JPA API, while still operate on the same data model and entities via our native POJO based API. This is a very powerful concept that covers not only JPA but all of our data access APIs, we call Same Data, Any API and it means that you can operate on the same data using a variety of APIs – besides our native POJO API and JPA. For example, you can read a JPA entity as a document (in case you want to treat it in a more loosely flexible manner, or use our C++ client side API from native clients to read this data.

See for Yourself

We have just published an comprehensive tutorial that explains the principles of the our JPA implementation using the good old Spring PetClinic sample app (we’ve added our own flavor to it :) ). It explains the data model considerations and shows how take advantage of data grid features such as space based remoting to optimize the data access of the application and use our

About Me

I work for GigaSpaces Technologies and have been implementing and architecting enterprise Java projects for the past 10 years or so.
Currently I run the product management at GigaSpaces, and blog mostly about scalability and distributed computing in the context of the problems we run into and try to solve in GigaSpaces.